Prediction of 30-Day Mortality Following Revision Total Hip and Knee Arthroplasty: Machine Learning Algorithms Outperform CARDE-B, 5-Item, and 6-Item Modified Frailty Index Risk Scores

Background Although risk calculators are used to prognosticate postoperative outcomes following revision total hip and knee arthroplasty (TJA), machine learning (ML) based predictive tools have emerged as a promising alternative for improved risk stratification. This study aimed to compare the predictive ability of ML models for 30-day mortality following revision TJA to that of traditional risk-assessment indices such as the CARDE-B score (congestive heart failure, albumin (<3.5 mg/dL), renal failure on dialysis, dependence for daily living, elderly (>65 years of age), and body mass index of <25 kg/m2), 5-item (5MFI), and 6-item modified frailty index (6MFI). Methods Adult patients undergoing revision TJA between 2013 and 2020 were selected from the ACS-NSQIP database and randomly split 80:20 to compose the training and validation cohorts. There were three ML models - extreme gradient boosting (XGB), random forest (RF), and elastic-net penalized logistic regression (NEPLR) - that were developed and evaluated using discrimination, calibration metrics, and accuracy. The discrimination of CARDE-B, 5MFI, and 6MFI scores was assessed individually and compared to that of ML models. Results All models were equally accurate (Brier score = 0.005) and demonstrated outstanding discrimination with similar areas (AUC) under the receiver operating characteristic curve (XGB = 0.94, RF = NEPLR = 0.93). The NEPLR was the best-calibrated model overall (slope = 0.54, intercept = -0.004). The CARDE-B had the highest discrimination among the scores (AUC = 0.89), followed by 6MFI (AUC = 0.80), and 5MFI (AUC = 0.68). Albumin < 3.5 mg/dL and BMI (< 30.15) were the most important predictors of 30-day mortality following revision TJA. Conclusions The ML models outperform traditional risk-assessment indices in predicting postoperative 30-day mortality after revision TJA. Our findings highlight the utility of ML for risk stratification in a clinical setting. The identification of hypoalbuminemia and BMI as prognostic markers may allow patient-specific perioperative optimization strategies to improve outcomes following revision TJA.